reshape包；tidyr,dplyr包；链式操作符%>%
一、reshape2包对数据格式进行转换
> x <- data.frame(k1 = c(NA,NA,3,4,5), k2 = c(1,NA,NA,4,5),
+ data = 1:5)
> y <- data.frame(k1 = c(NA,2,NA,4,5), k2 = c(NA,NA,3,4,5),
+ data = 1:5)
> x
k1 k2 data
1 NA 1 1
2 NA NA 2
3 3 NA 3
4 4 4 4
5 5 5 5
> y
k1 k2 data
1 NA NA 1
2 2 NA 2
3 NA 3 3
4 4 4 4
5 5 5 5
如上两个数据框，无法直接用
rbind和cbind进行合并，会乱
- merge()处理
可以根据一个或多个公有的向量进行合并
#根据k1进行合并
> merge(x,y,by = "k1")
k1 k2.x data.x k2.y data.y
1 4 4 4 4 4
2 5 5 5 5 5
3 NA 1 1 NA 1
4 NA 1 1 3 3
5 NA NA 2 NA 1
6 NA NA 2 3 3
#incomparable = T，表示丢掉NA
>> merge(x, y, by = "k2",incomparables = NA)
> merge(x, y, by = "k2",incomparables = NA)
k2 k1.x data.x k1.y data.y
1 4 4 4 4 4
2 5 5 5 5 5
#根据k1，k2进行合并
> merge(x, y, by = c("k1","k2"))
k1 k2 data.x data.y
1 4 4 4 4
2 5 5 5 5
3 NA NA 2 1
- reshape包
reshape包重构整个数据的万能数据包，最新的是reshape2
> install.packages("reshape2")
> library(reshape2)
#与Excel中数据透视表功能类似
> help(package = "reshape2")
help_reshape2.png
（1）melt对宽数据进行处理，得到长数据
> head(airquality)
Ozone Solar.R Wind Temp Month Day
1 41 190 7.4 67 5 1
2 36 118 8.0 72 5 2
3 12 149 12.6 74 5 3
4 18 313 11.5 62 5 4
5 NA NA 14.3 56 5 5
6 28 NA 14.9 66 5 6
#将列名首字母改为小写
> names(airquality) <- tolower(names(airquality))
> head(airquality)
ozone solar.r wind temp month day
1 41 190 7.4 67 5 1
2 36 118 8.0 72 5 2
3 12 149 12.6 74 5 3
4 18 313 11.5 62 5 4
5 NA NA 14.3 56 5 5
6 28 NA 14.9 66 5 6
#使用melt函数处理数据
> melt(airquality)
> aql <- melt(airquality)
#融合后，每一行都是标识符和变量的组合，不能有重复项。数据变为三列。
#其中variable是因子类型
#这也就是宽数据变为长数据的过程
> head(aql)
variable value
1 ozone 41
2 ozone 36
3 ozone 12
4 ozone 18
5 ozone NA
6 ozone 28
> head(aql,50)
variable value
1 ozone 41
2 ozone 36
3 ozone 12
4 ozone 18
5 ozone NA
6 ozone 28
7 ozone 23
8 ozone 19
9 ozone 8
10 ozone NA
11 ozone 7
12 ozone 16
13 ozone 11
14 ozone 14
15 ozone 18
16 ozone 14
17 ozone 34
18 ozone 6
19 ozone 30
20 ozone 11
21 ozone 1
22 ozone 11
23 ozone 4
24 ozone 32
25 ozone NA
26 ozone NA
27 ozone NA
28 ozone 23
29 ozone 45
30 ozone 115
31 ozone 37
32 ozone NA
33 ozone NA
34 ozone NA
35 ozone NA
36 ozone NA
37 ozone NA
38 ozone 29
39 ozone NA
40 ozone 71
41 ozone 39
42 ozone NA
43 ozone NA
44 ozone 23
45 ozone NA
46 ozone NA
47 ozone 21
48 ozone 37
49 ozone 20
50 ozone 12
#需要设置，month和day是用来当做ID，其余四个作为变量值。
#ID就是用来区分不同行数之间的变量
#重要！需要区分哪部分作为行的观测值，哪部分作为列的观测值
> aql <- melt(airquality,id.vars = c("month","day"))
> head(aql,50)
month day variable value
1 5 1 ozone 41
2 5 2 ozone 36
3 5 3 ozone 12
4 5 4 ozone 18
5 5 5 ozone NA
6 5 6 ozone 28
7 5 7 ozone 23
8 5 8 ozone 19
9 5 9 ozone 8
10 5 10 ozone NA
11 5 11 ozone 7
12 5 12 ozone 16
13 5 13 ozone 11
14 5 14 ozone 14
15 5 15 ozone 18
16 5 16 ozone 14
17 5 17 ozone 34
18 5 18 ozone 6
19 5 19 ozone 30
20 5 20 ozone 11
21 5 21 ozone 1
22 5 22 ozone 11
23 5 23 ozone 4
24 5 24 ozone 32
25 5 25 ozone NA
26 5 26 ozone NA
27 5 27 ozone NA
28 5 28 ozone 23
29 5 29 ozone 45
30 5 30 ozone 115
31 5 31 ozone 37
32 6 1 ozone NA
33 6 2 ozone NA
34 6 3 ozone NA
35 6 4 ozone NA
36 6 5 ozone NA
37 6 6 ozone NA
38 6 7 ozone 29
39 6 8 ozone NA
40 6 9 ozone 71
41 6 10 ozone 39
42 6 11 ozone NA
43 6 12 ozone NA
44 6 13 ozone 23
45 6 14 ozone NA
46 6 15 ozone NA
47 6 16 ozone 21
48 6 17 ozone 37
49 6 18 ozone 20
50 6 19 ozone 12
（2）cast将长数据变为宽数据
reshape2将cast分为了几种
①dcast：处理数据框，读取melt的结果，根据提供的公式进行数据融合。
cast.png
参数：formula，融合后的数据格式。
"~"在R找那个表示相关联，说明而这有关系，但不一定是相等
#重录数据
> aqw <- dcast(aql,month ~ variable, fun.aggregate = mean,na.rm = TRUE)
> head(aqw)
month ozone solar.r wind temp
1 5 23.61538 181.2963 11.622581 65.54839
2 6 29.44444 190.1667 10.266667 79.10000
3 7 59.11538 216.4839 8.941935 83.90323
4 8 59.96154 171.8571 8.793548 83.96774
5 9 31.44828 167.4333 10.180000 76.90000
#fun.aggregate也可以设置为sum等其他函数
> aqw <- dcast(aql,month ~ variable, fun.aggregate = sum,na.rm = TRUE)
> head(aqw)
month ozone solar.r wind temp
1 5 614 4895 360.3 2032
2 6 265 5705 308.0 2373
3 7 1537 6711 277.2 2601
4 8 1559 4812 272.6 2603
5 9 912 5023 305.4 2307
②acast：返回向量，矩阵或数组
二、tidyr&dplyr数据转换
这两个包相对于reshape2，操作更加简便。
安装：
> install.packages(c("tidyr","dplyr"))
> library(tidyr)
> library(dplyr)
- tidyr包(Tidy Messy Data)
Overview
The goal of tidyr is to help you create tidy data. Tidy data is data where:
- Every column is variable.
- Every row is an observation.
- Every cell is a single value.
Tidy data describes a standard way of storing data that is used wherever possible throughout the tidyverse. If you ensure that your data is tidy, you’ll spend less time fighting with the tools and more time working on your analysis. Learn more about tidy data in[vignette("tidy-data")](https://tidyr.tidyverse.org/articles/tidy-data.html)
（1）gather(),将宽数据转化为长数据，类似reshape2的melt()
（2）spread()，将长数据转化为宽数据，类似reshape2的cast()
（3）unit()，将多列合并为一列
（4）separate()，将一列分为多列
以mtcars数据集作为演示
#mtcars每一列是变量，每一行是观测值
#取部分数据进行演示
> tdata <- mtcars[1:10,1:3]
#汽车名是以行名存在的，对数据进行处理，将行名添加到数据中
> tdata <- data.frame(names = rownames(tdata),tdata)
names mpg cyl disp
Mazda RX4 Mazda RX4 21.0 6 160.0
Mazda RX4 Wag Mazda RX4 Wag 21.0 6 160.0
Datsun 710 Datsun 710 22.8 4 108.0
Hornet 4 Drive Hornet 4 Drive 21.4 6 258.0
Hornet Sportabout Hornet Sportabout 18.7 8 360.0
Valiant Valiant 18.1 6 225.0
Duster 360 Duster 360 14.3 8 360.0
Merc 240D Merc 240D 24.4 4 146.7
Merc 230 Merc 230 22.8 4 140.8
Merc 280 Merc 280 19.2 6 167.6
(1)gather()函数
优点：固定列不变，其他列进行转换
gather().png
> gather(tdata, key = "Key",value = "Value",cyl,disp,mpg)
> gather(tdata, key = "Key",value = "Value",cyl,disp,mpg)
names Key Value
1 Mazda RX4 cyl 6.0
2 Mazda RX4 Wag cyl 6.0
3 Datsun 710 cyl 4.0
4 Hornet 4 Drive cyl 6.0
5 Hornet Sportabout cyl 8.0
6 Valiant cyl 6.0
7 Duster 360 cyl 8.0
8 Merc 240D cyl 4.0
9 Merc 230 cyl 4.0
10 Merc 280 cyl 6.0
11 Mazda RX4 disp 160.0
12 Mazda RX4 Wag disp 160.0
13 Datsun 710 disp 108.0
14 Hornet 4 Drive disp 258.0
15 Hornet Sportabout disp 360.0
16 Valiant disp 225.0
17 Duster 360 disp 360.0
18 Merc 240D disp 146.7
19 Merc 230 disp 140.8
20 Merc 280 disp 167.6
21 Mazda RX4 mpg 21.0
22 Mazda RX4 Wag mpg 21.0
23 Datsun 710 mpg 22.8
24 Hornet 4 Drive mpg 21.4
25 Hornet Sportabout mpg 18.7
26 Valiant mpg 18.1
27 Duster 360 mpg 14.3
28 Merc 240D mpg 24.4
29 Merc 230 mpg 22.8
30 Merc 280 mpg 19.2
# : 表示将某些列聚集到同一列中
> gather(tdata, key = "Key",value = "Value",cyl:disp,mpg)
# - 减去不需要的列
> gather(tdata, key = "Key",value = "Value",cyl,-disp)
names mpg disp Key Value
1 Mazda RX4 21.0 160.0 cyl 6
2 Mazda RX4 Wag 21.0 160.0 cyl 6
3 Datsun 710 22.8 108.0 cyl 4
4 Hornet 4 Drive 21.4 258.0 cyl 6
5 Hornet Sportabout 18.7 360.0 cyl 8
6 Valiant 18.1 225.0 cyl 6
7 Duster 360 14.3 360.0 cyl 8
8 Merc 240D 24.4 146.7 cyl 4
9 Merc 230 22.8 140.8 cyl 4
10 Merc 280 19.2 167.6 cyl 6
#若敲列的名字容易敲错，也可以敲编号
> gdata <- gather(tdata, key = "Key",value = "Value",2:4)
（2） spread()函数
与gather相反
spread.png
#首先确定哪一列打散
> spread(gdata, key = "Key",value = "Value")
names cyl disp mpg
1 Datsun 710 4 108.0 22.8
2 Duster 360 8 360.0 14.3
3 Hornet 4 Drive 6 258.0 21.4
4 Hornet Sportabout 8 360.0 18.7
5 Mazda RX4 6 160.0 21.0
6 Mazda RX4 Wag 6 160.0 21.0
7 Merc 230 4 140.8 22.8
8 Merc 240D 4 146.7 24.4
9 Merc 280 6 167.6 19.2
10 Valiant 6 225.0 18.1
（3）separate()
可以将一列拆分成多列
separate.png
> df <- data.frame(x = c(NA,"a.b","a.d","b.c"))
> df
x
1 <NA>
2 a.b
3 a.d
4 b.c
#将这一列，按“.”分为两列
> separate(df,col = x,into = c("A","B"))
A B
1 <NA> <NA>
2 a b
3 a d
4 b c
> df <- data.frame(x = c(NA,"a.b-c","a-d","b-c"))
> separate(df,col = x,into = c("A","B"))
A B
1 <NA> <NA>
2 a b
3 a d
4 b c
Warning message:
Expected 2 pieces. Additional pieces discarded in 1 rows [2].
#可看出第一个还是按“.”分割，c的值被丢掉了
#指定sep参数为连字符
> separate(df,col = x,into = c("A","B"),sep = "-")
A B
1 <NA> <NA>
2 a.b c
3 a d
4 b c
（4）unite()
将separate之后的数据连接起来
unite.png
> x <- separate(df,col = x,into = c("A","B"),sep = "-")
> unite(x,col = "AB",A,B,sep = "-")
AB
1 NA-NA
2 a.b-c
3 a-d
4 b-c
- dplyr包
不仅可以对单个表格操作，还可以对双表格操作
[1] "%>%" "across" "add_count"
[4] "add_count_" "add_row" "add_rownames"
[7] "add_tally" "add_tally_" "all_equal"
[10] "all_of" "all_vars" "anti_join"
[13] "any_of" "any_vars" "arrange"
[16] "arrange_" "arrange_all" "arrange_at"
[19] "arrange_if" "as.tbl" "as_data_frame"
[22] "as_label" "as_tibble" "auto_copy"
[25] "band_instruments" "band_instruments2" "band_members"
[28] "bench_tbls" "between" "bind_cols"
[31] "bind_rows" "c_across" "case_when"
[34] "changes" "check_dbplyr" "coalesce"
[37] "collapse" "collect" "combine"
[40] "common_by" "compare_tbls" "compare_tbls2"
[43] "compute" "contains" "copy_to"
[46] "count" "count_" "cumall"
[49] "cumany" "cume_dist" "cummean"
[52] "cur_column" "cur_data" "cur_data_all"
[55] "cur_group" "cur_group_id" "cur_group_rows"
[58] "current_vars" "data_frame" "data_frame_"
[61] "db_analyze" "db_begin" "db_commit"
[64] "db_create_index" "db_create_indexes" "db_create_table"
[67] "db_data_type" "db_desc" "db_drop_table"
[70] "db_explain" "db_has_table" "db_insert_into"
[73] "db_list_tables" "db_query_fields" "db_query_rows"
[76] "db_rollback" "db_save_query" "db_write_table"
[79] "dense_rank" "desc" "dim_desc"
[82] "distinct" "distinct_" "distinct_all"
[85] "distinct_at" "distinct_if" "distinct_prepare"
[88] "do" "do_" "dplyr_col_modify"
[91] "dplyr_reconstruct" "dplyr_row_slice" "ends_with"
[94] "enexpr" "enexprs" "enquo"
[97] "enquos" "ensym" "ensyms"
[100] "eval_tbls" "eval_tbls2" "everything"
[103] "explain" "expr" "failwith"
[106] "filter" "filter_" "filter_all"
[109] "filter_at" "filter_if" "first"
[112] "frame_data" "full_join" "funs"
[115] "funs_" "glimpse" "group_by"
[118] "group_by_" "group_by_all" "group_by_at"
[121] "group_by_drop_default" "group_by_if" "group_by_prepare"
[124] "group_cols" "group_data" "group_indices"
[127] "group_indices_" "group_keys" "group_map"
[130] "group_modify" "group_nest" "group_rows"
[133] "group_size" "group_split" "group_trim"
[136] "group_vars" "group_walk" "grouped_df"
[139] "groups" "id" "ident"
[142] "if_else" "inner_join" "intersect"
[145] "is.grouped_df" "is.src" "is.tbl"
[148] "is_grouped_df" "lag" "last"
[151] "last_col" "lead" "left_join"
[154] "location" "lst" "lst_"
[157] "make_tbl" "matches" "min_rank"
[160] "mutate" "mutate_" "mutate_all"
[163] "mutate_at" "mutate_each" "mutate_each_"
[166] "mutate_if" "n" "n_distinct"
[169] "n_groups" "na_if" "near"
[172] "nest_by" "nest_join" "new_grouped_df"
[175] "nth" "ntile" "num_range"
[178] "one_of" "order_by" "percent_rank"
[181] "progress_estimated" "pull" "quo"
[184] "quo_name" "quos" "recode"
[187] "recode_factor" "relocate" "rename"
[190] "rename_" "rename_all" "rename_at"
[193] "rename_if" "rename_vars" "rename_vars_"
[196] "rename_with" "right_join" "row_number"
[199] "rows_delete" "rows_insert" "rows_patch"
[202] "rows_update" "rows_upsert" "rowwise"
[205] "same_src" "sample_frac" "sample_n"
[208] "select" "select_" "select_all"
[211] "select_at" "select_if" "select_var"
[214] "select_vars" "select_vars_" "semi_join"
[217] "setdiff" "setequal" "show_query"
[220] "slice" "slice_" "slice_head"
[223] "slice_max" "slice_min" "slice_sample"
[226] "slice_tail" "sql" "sql_escape_ident"
[229] "sql_escape_string" "sql_join" "sql_select"
[232] "sql_semi_join" "sql_set_op" "sql_subquery"
[235] "sql_translate_env" "src" "src_df"
[238] "src_local" "src_mysql" "src_postgres"
[241] "src_sqlite" "src_tbls" "starts_with"
[244] "starwars" "storms" "summarise"
[247] "summarise_" "summarise_all" "summarise_at"
[250] "summarise_each" "summarise_each_" "summarise_if"
[253] "summarize" "summarize_" "summarize_all"
[256] "summarize_at" "summarize_each" "summarize_each_"
[259] "summarize_if" "sym" "syms"
[262] "tally" "tally_" "tbl"
[265] "tbl_df" "tbl_nongroup_vars" "tbl_ptype"
[268] "tbl_sum" "tbl_vars" "tibble"
[271] "top_frac" "top_n" "transmute"
[274] "transmute_" "transmute_all" "transmute_at"
[277] "transmute_if" "tribble" "trunc_mat"
[280] "type_sum" "ungroup" "union"
[283] "union_all" "validate_grouped_df" "vars"
[286] "with_groups" "with_order" "wrap_dbplyr_obj"
##一共有288个函数
（1）对单个表格进行操作
①过滤filter
#将长度小于等于7的数据过滤掉
> dplyr::filter(iris,Sepal.Length>7)
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1 7.1 3.0 5.9 2.1 virginica
2 7.6 3.0 6.6 2.1 virginica
3 7.3 2.9 6.3 1.8 virginica
4 7.2 3.6 6.1 2.5 virginica
5 7.7 3.8 6.7 2.2 virginica
6 7.7 2.6 6.9 2.3 virginica
7 7.7 2.8 6.7 2.0 virginica
8 7.2 3.2 6.0 1.8 virginica
9 7.2 3.0 5.8 1.6 virginica
10 7.4 2.8 6.1 1.9 virginica
11 7.9 3.8 6.4 2.0 virginica
12 7.7 3.0 6.1 2.3 virginica
由于dplyr的函数太多，所以一般运用函数时，是dplyr::函数名，这样就不会出现歧义
②去除重复行distinct()
类似unique
> dplyr::distinct(rbind(iris[1:10,],iris[1:15,]))
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1 5.1 3.5 1.4 0.2 setosa
2 4.9 3.0 1.4 0.2 setosa
3 4.7 3.2 1.3 0.2 setosa
4 4.6 3.1 1.5 0.2 setosa
5 5.0 3.6 1.4 0.2 setosa
6 5.4 3.9 1.7 0.4 setosa
7 4.6 3.4 1.4 0.3 setosa
8 5.0 3.4 1.5 0.2 setosa
9 4.4 2.9 1.4 0.2 setosa
10 4.9 3.1 1.5 0.1 setosa
11 5.4 3.7 1.5 0.2 setosa
12 4.8 3.4 1.6 0.2 setosa
13 4.8 3.0 1.4 0.1 setosa
14 4.3 3.0 1.1 0.1 setosa
15 5.8 4.0 1.2 0.2 setosa
③切片slice()
用于取出数据的任意行
> dplyr::slice(iris,10:15)
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1 4.9 3.1 1.5 0.1 setosa
2 5.4 3.7 1.5 0.2 setosa
3 4.8 3.4 1.6 0.2 setosa
4 4.8 3.0 1.4 0.1 setosa
5 4.3 3.0 1.1 0.1 setosa
6 5.8 4.0 1.2 0.2 setosa
④随机取样sample_n()
> dplyr::sample_n(iris,10)
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1 6.9 3.1 4.9 1.5 versicolor
2 5.4 3.9 1.7 0.4 setosa
3 5.9 3.0 4.2 1.5 versicolor
4 6.3 2.9 5.6 1.8 virginica
5 5.1 3.5 1.4 0.2 setosa
6 6.1 2.9 4.7 1.4 versicolor
7 5.5 3.5 1.3 0.2 setosa
8 5.7 2.9 4.2 1.3 versicolor
9 5.8 2.8 5.1 2.4 virginica
10 4.8 3.4 1.6 0.2 setosa
⑤按比例随机选取sample_frac()
> dplyr::sample_frac(iris,0.1)
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1 6.7 3.3 5.7 2.1 virginica
2 5.5 2.4 3.8 1.1 versicolor
3 5.1 3.5 1.4 0.2 setosa
4 4.9 2.4 3.3 1.0 versicolor
5 5.5 3.5 1.3 0.2 setosa
6 4.8 3.0 1.4 0.3 setosa
7 5.6 3.0 4.1 1.3 versicolor
8 6.7 3.3 5.7 2.5 virginica
9 4.8 3.1 1.6 0.2 setosa
10 6.1 2.6 5.6 1.4 virginica
11 6.0 2.9 4.5 1.5 versicolor
12 4.6 3.6 1.0 0.2 setosa
13 4.4 3.2 1.3 0.2 setosa
14 4.4 3.0 1.3 0.2 setosa
15 6.5 3.0 5.8 2.2 virginica
⑥排序arrange()
#按花的长度进行排序
> dplyr::arrange(iris,Sepal.Length)
#按相反方向进行排序
> dplyr::arrange(iris,desc(Sepal.Length))
⑦取子集select()
#理解select功能
> ?select
⑧统计函数summarise()
> summarise(iris,avg = mean(Sepal.Length))
avg
1 5.843333
#还可将mean换成sum，计算总长度
链式操作符%>%
用于实现将一个函数的输出传递给下一个函数，作为下一个函数的输入，相当于是管道函数
在Rstudio中可以使用Ctrl+shift+M快捷键输出
> head(mtcars,20)
mpg cyl disp hp drat wt qsec vs am gear carb
Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
#取出数据第十一到第十二行
> head(mtcars,20) %>% tail(10)
mpg cyl disp hp drat wt qsec vs am gear carb
Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
⑨分组group_by
> dplyr::group_by(iris,Species)
# A tibble: 150 x 5
# Groups: Species [3]
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
<dbl> <dbl> <dbl> <dbl> <fct>
1 5.1 3.5 1.4 0.2 setosa
2 4.9 3 1.4 0.2 setosa
3 4.7 3.2 1.3 0.2 setosa
4 4.6 3.1 1.5 0.2 setosa
5 5 3.6 1.4 0.2 setosa
6 5.4 3.9 1.7 0.4 setosa
7 4.6 3.4 1.4 0.3 setosa
8 5 3.4 1.5 0.2 setosa
9 4.4 2.9 1.4 0.2 setosa
10 4.9 3.1 1.5 0.1 setosa
# ... with 140 more rows
> iris %>% group_by(Species)
# A tibble: 150 x 5
# Groups: Species [3]
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
<dbl> <dbl> <dbl> <dbl> <fct>
1 5.1 3.5 1.4 0.2 setosa
2 4.9 3 1.4 0.2 setosa
3 4.7 3.2 1.3 0.2 setosa
4 4.6 3.1 1.5 0.2 setosa
5 5 3.6 1.4 0.2 setosa
6 5.4 3.9 1.7 0.4 setosa
7 4.6 3.4 1.4 0.3 setosa
8 5 3.4 1.5 0.2 setosa
9 4.4 2.9 1.4 0.2 setosa
10 4.9 3.1 1.5 0.1 setosa
# ... with 140 more rows
##再计算一下平均值
> iris %>% group_by(Species) %>% summarise(avg=mean(Sepal.Width))
# A tibble: 3 x 2
Species avg
* <fct> <dbl>
1 setosa 3.43
2 versicolor 2.77
3 virginica 2.97
##再按结果大小进行排序
> iris %>% group_by(Species) %>% summarise(avg=mean(Sepal.Width)) %>% arrange(avg)
# A tibble: 3 x 2
Species avg
<fct> <dbl>
1 versicolor 2.77
2 virginica 2.97
3 setosa 3.43
⑩添加变量mutate()
> dplyr::mutate(iris,new = Sepal.Length+Petal.Length)
Sepal.Length Sepal.Width Petal.Length Petal.Width Species new
1 5.1 3.5 1.4 0.2 setosa 6.5
2 4.9 3.0 1.4 0.2 setosa 6.3
3 4.7 3.2 1.3 0.2 setosa 6.0
4
……
（2）对双表格操作
主要是如何将两个表格进行整合
多种方式：左连接，右连接，内连接，全连接，半连接，反连接等
#创建两个数据框
> a=data.frame(x1=c("A","B","C"),x2=c(1,2,3))
> b=data.frame(x1=c("A","B","D"),x3=c(T,F,T))
> a
x1 x2
1 A 1
2 B 2
3 C 3
> b
x1 x3
1 A TRUE
2 B FALSE
3 D TRUE
①左连接
以左边的表为基础
> dplyr::left_join(a,b,by="x1")
x1 x2 x3
1 A 1 TRUE
2 B 2 FALSE
3 C 3 NA
②右连接
> dplyr::right_join(a,b,by="x1")
x1 x2 x3
1 A 1 TRUE
2 B 2 FALSE
3 D NA TRUE
③内连接（取x1的交集）
全连接（取x1的并集）
> dplyr::full_join(a,b,by="x1")
x1 x2 x3
1 A 1 TRUE
2 B 2 FALSE
3 C 3 NA
4 D NA TRUE
> dplyr::inner_join(a,b,by="x1")
x1 x2 x3
1 A 1 TRUE
2 B 2 FALSE
④半连接（根据右侧表内容，对左侧表进行过滤，即将a中与b的交集取出来）
反连接（根据右侧表内容过滤，但是是将a中与b的补集取出来）
主要是进行集合的各种运算
> dplyr::semi_join(a,b,by="x1")
x1 x2
1 A 1
2 B 2
> dplyr::anti_join(a,b,by="x1")
x1 x2
1 C 3
数据集合并
intrsect union secdev
本质：集合运算
以mtcars数据集作为演示
#slice取出的数据不包含行名
#使用mute()为数据集添加一行
> mtcars <- mutate(mtcars,Model=rownames(mtcars))
> first <- slice(mtcars,1:20)
> second <- slice (mtcars,10:30)
##这两者之间有重合部分
##接下来测试这些函数
#intersect()取交集
> intersect(first, second)
mpg cyl disp hp drat wt qsec vs am gear carb Model
Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 Merc 280
Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 Merc 280C
Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 Merc 450SE
Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 Merc 450SL
Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3 Merc 450SLC
Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4 Cadillac Fleetwood
Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4 Lincoln Continental
Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4 Chrysler Imperial
Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1 Fiat 128
Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 Honda Civic
Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1 Toyota Corolla
#union_all取并集
> union_all(first, second)
mpg cyl disp hp drat wt qsec vs am gear carb Model
Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 Mazda RX4
Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 Mazda RX4 Wag
Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 Datsun 710
Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 Hornet 4 Drive
Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 Hornet Sportabout
Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 Valiant
Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 Duster 360
Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 Merc 240D
Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 Merc 230
Merc 280...10 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 Merc 280
Merc 280C...11 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 Merc 280C
Merc 450SE...12 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 Merc 450SE
Merc 450SL...13 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 Merc 450SL
Merc 450SLC...14 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3 Merc 450SLC
Cadillac Fleetwood...15 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4 Cadillac Fleetwood
Lincoln Continental...16 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4 Lincoln Continental
Chrysler Imperial...17 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4 Chrysler Imperial
Fiat 128...18 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1 Fiat 128
Honda Civic...19 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 Honda Civic
Toyota Corolla...20 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1 Toyota Corolla
Merc 280...21 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 Merc 280
Merc 280C...22 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 Merc 280C
Merc 450SE...23 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 Merc 450SE
Merc 450SL...24 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 Merc 450SL
Merc 450SLC...25 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3 Merc 450SLC
Cadillac Fleetwood...26 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4 Cadillac Fleetwood
Lincoln Continental...27 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4 Lincoln Continental
Chrysler Imperial...28 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4 Chrysler Imperial
Fiat 128...29 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1 Fiat 128
Honda Civic...30 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 Honda Civic
Toyota Corolla...31 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1 Toyota Corolla
Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1 Toyota Corona
Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2 Dodge Challenger
AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2 AMC Javelin
Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4 Camaro Z28
Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2 Pontiac Firebird
Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 Fiat X1-9
Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 Porsche 914-2
Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 Lotus Europa
Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4 Ford Pantera L
Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6 Ferrari Dino
#union取非冗余的并集
> union(first, second)
mpg cyl disp hp drat wt qsec vs am gear carb Model
Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 Mazda RX4
Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 Mazda RX4 Wag
Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 Datsun 710
Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 Hornet 4 Drive
Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 Hornet Sportabout
Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 Valiant
Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 Duster 360
Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 Merc 240D
Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 Merc 230
Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 Merc 280
Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 Merc 280C
Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 Merc 450SE
Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 Merc 450SL
Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3 Merc 450SLC
Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4 Cadillac Fleetwood
Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4 Lincoln Continental
Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4 Chrysler Imperial
Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1 Fiat 128
Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 Honda Civic
Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1 Toyota Corolla
Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1 Toyota Corona
Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2 Dodge Challenger
AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2 AMC Javelin
Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4 Camaro Z28
Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2 Pontiac Firebird
Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 Fiat X1-9
Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 Porsche 914-2
Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 Lotus Europa
Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4 Ford Pantera L
Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6 Ferrari Dino
#setdiff()取first补集
> setdiff(first, second)
mpg cyl disp hp drat wt qsec vs am gear carb Model
Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 Mazda RX4
Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 Mazda RX4 Wag
Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 Datsun 710
Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 Hornet 4 Drive
Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 Hornet Sportabout
Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 Valiant
Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 Duster 360
Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 Merc 240D
Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 Merc 230
#取second的补集
> setdiff(second, first)
mpg cyl disp hp drat wt qsec vs am gear carb Model
Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1 Toyota Corona
Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2 Dodge Challenger
AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2 AMC Javelin
Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4 Camaro Z28
Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2 Pontiac Firebird
Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 Fiat X1-9
Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 Porsche 914-2
Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 Lotus Europa
Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4 Ford Pantera L
Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6 Ferrari Dino
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